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Record W4253972603 · doi:10.32920/ryerson.14661003.v1

The perceptions of five urban early childhood educators on the needs of young English language learners in child care centres : best practices in the field

2021· preprint· en· W4253972603 on OpenAlexaffabout
Olayinka Fakunle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentUniversity of Victoria
Fundersnot available
KeywordsPerceptionSocializationHome languagePedagogyPsychologyGrounded theoryLanguage acquisitionLanguage assessmentQualitative researchEnglish languageMedical educationMedicineSociologyMathematics educationDevelopmental psychology

Abstract

fetched live from OpenAlex

This study investigated the perceptions of Early Childhood Educators on the needs of English language learners in childcare centres in Toronto, Canada. A modified grounded theory methodology was utilized in the study. Interviews were held with 5 Early Childhood Educators; these interviews were transcribed and coded. 5 themes arose from this qualitative analysis: sensitivity, communication, school readiness, home language retention and socialization. Results indicate that ECEs perceived that English language learners thrive in a caring environment with staff that will guide and support them in language learning, and where the use of their first language is encouraged and used to build the skills in the second language. Recommendations include ensuring the presence of staff that have a first language match with the English language learners, and can speak the same language with the children.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.667

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.308
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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